The prior art, to include U.S. patent application Ser. No. 10/892,747, filed Jul. 15, 2004, to Howard et al, “System and Method for Automated Search by Distributed Elements” (which is herein incorporated by reference) generally includes a number of methods for predicting the behavior of an object or “target” in a known environment or area of interest or area of operation (“AOR”) using a set of particle filters, one for each hypothesized identity. These predictive methodologies are an algorithmic way to associate an identity and a geographic position estimate with a string of measurements representing a target, usually a moving target. It is important for the tracker to keep a single track associated with a single target because, over time, the track acquires additional attributes (either manually or automatically), including an identity affiliation (e.g. friend or foe) and kinematic information. The problem of single tracking has historically been made difficult by maneuvering targets, large numbers of targets, and targets in close proximity (such as target tracks that cross).
Two additional factors also contribute to the difficulty of maintaining a single track per target, specifically: (1) the highly maneuverable nature of ground targets; and (2) the intermittent coverage caused by either obscuration (such as mountains, buildings, tunnels or foliage), or by the need to divert surveillance attention elsewhere for some period of time. In order to adequately address these difficulties and minimize their impact, it would be helpful to have some idea of where the target being tracked is likely located at some time in the future, so that it can be reacquired and the track updated when coverage is again established. Alternatively, when target motion can be continually tracked, behavior models allow inference of the possible identity/intent of the target, allowing targets of interest to be separated from targets which are of no interest.
The current state of the art with regard to predictive tracking and identification of known or suspected targets includes one of two fundamentally distinct approaches. The first approach defines particles which can “diffuse” in an environment. Particles are initialized with a track state vector, and move with a velocity consistent with the state vector plus a random perturbation. The expectation is that when it is time to reacquire the track it will be near the location of one of the particles. Generally speaking, this approach works best in association with only very short duration tracking interruptions. The effectiveness of this method degrades rapidly when the duration of the interruption approaches a few minutes, primarily due to other factors beyond the basic track state vector which come to dominate the position of the target.
The second approach currently available constrains the search area by mobility. If the target is a vehicle on the ground, it will be able to travel on roads and to a lesser extent off roads, based on the mobility of the vehicle itself (e.g. ground clearance, hill climbing capability, max speed, etc.), and the properties of the terrain. Given information about a vehicle's capabilities and the terrain features, a mobility cost surface can be calculated. A simplistic form of the surface identifies areas that are passable (“go”) and not passable (“no-go”). More elegant forms of the mobility cost surface may be used and may include such considerations as maximum/minimum speed and a mobility “cost” in terms of the difficulty and desirability to move in a certain direction. Clearly, a well calculated mobility cost surface can enhance and simplify the tracking analysis. For example, in very mountainous terrain mobility is severely limited for most if not all vehicle types, therefore, the search space required for tracking may be significantly constrained. Nonetheless, the methodology of mobility constraint is limited in its application, especially in areas where the terrain is basically flat and passable by most vehicles (for example, a flat desert area, or nearly any body of water).
It may be possible, in some applications of the prior art, to combine the concepts of particle diffusion and mobility constraint, so that particles are constrained by the mobility/cost surface to move preferentially in low cost directions, and to move with velocities constrained by the maximum velocity applicable to their current direction. Even with this combined, enhanced capability, the problems of track discontinuity and confidence in identification exist, particularly when a network of roads is present or when the cost surface is very uniform.
Hence a need exists for a method and system for target tracking and identification which will address one or more of the drawbacks identified above.